Senior Analytics Engineer

Lob

7h•$152,500 - $170,000•Remote

About The Position

As a member of the Data team, you will develop data pipelines and build data models that enable the entire company to discover actionable insights and make rapid data-driven decisions. You will work with colleagues in Product and Engineering, as well as stakeholders across teams such as Sales, Finance, Marketing, and Operations to identify data needs and develop durable solutions that help answer business questions.

Requirements

5+ years of Analytics Engineering experience, including a background in ELT frameworks, OLAP modeling, data visualization, and data governance.
5+ years of SQL experience: at least one big data warehouse system such as Redshift, Snowflake, or BigQuery. Snowflake preferred.
3+ years of experience operating live production systems using dbt and Python.
3+ years of BI Tool experience: at least one analytics platform such as Omni, Looker, or Tableau. Omni preferred.
1+ years of experience curating semantic data models to enable conversational BI.
Expertise in modern data visualization techniques: Ability to apply best practices to clearly and insightfully communicate data to drive decisions.
Empathy and effective communication skills: Ability to explain complex analytical issues to both technical and non-technical audiences.
Strong interpretive skills: Ability to deconstruct complex source data to compose curated models that can be explored by stakeholders.
Product mindset: Ability to build data systems that will be used to generate insights for years to come, not just one-off analyses.

Nice To Haves

dbt Developer certification is a plus.
Domain knowledge in Supply Chain, Finance, B2B SaaS, or a similar relevant field is a plus.

Responsibilities

Partner with stakeholders to identify problems, create insights, and develop durable data solutions.
Build robust data pipelines to curate analytical data from internal and external systems.
Curate the semantic data layer to bridge raw tables and business context and enable AI use cases.
Develop dashboards and alerting systems that track and monitor business performance, with a focus on clear and insightful data visualizations.
Exemplify analytics engineering best practices, such as modularity, testing, cataloging, and version control.
Champion data governance, security, privacy, and retention policies to protect end users, customers, and Lob.